The present invention relates generally to the field of spectral harvesting systems for RF (radio frequency) emissions from RF emitters.
Spectral harvesting systems which harvest RF spectral emissions are known. In spectral harvesting, RF signals in a target region are detected, and target RF emitters are determined and identified. The type of target may then be determined based on the type of RF signal (modulation, baud rate, frequency, etc.), emitted by the RF emitter.
Detection of target RF transmitters depends on the RF band spectral noise. Conventionally, RF signal detection systems rely on an RF communication signal to be absent in order to estimate the RF band spectral noise.
In the area of audio signal enhancement, it is known to detect and enhance an audio signal in the presence of non-stationary noise where the level of audio signals varies in time. Audio signal processing techniques such as minima controlled recursive averaging (MCRA) provide robust noise estimates in highly non-stationary audio environments such as street, train, or cocktail noise environments.
Audio signal processing techniques such as MCRA, however, are not appropriate for RF signal processing since MCRA was formulated based on the knowledge of the cadence of human speech.